A CNN is a large scale network of an aggregate of regularly spaced circuit clones, called cells, which communicate with each other non-linearly only through one or more layers of the nearest neighbors in real time.
Conventional digital computation methods have run into a serious speed bottleneck due to their serial nature. To overcome this problem, a new computation model, called "neural networks," has been proposed, which is based on some aspects of neurobiology and adapted to integrated circuits. The key features of neural networks are asynchronous parallel processing, continuous-time dynamics, and global interaction of network elements. Some encouraging, if not impressive, applications of neural networks have been proposed for various fields, such as optimization, linear and nonlinear programming, associative memory, pattern recognition and computer vision.
A new circuit architecture, called a cellular neural network, possesses some of the key features of neural networks and has important potential applications in such areas as image processing and pattern recognition.
The structure of cellular neural networks is similar to that found in cellular automata; namely, any cell in a cellular neural network is connected only to its neighbor cells. The adjacent cells can interact directly with each other. Cells not directly connected together may affect each other indirectly because of the propagation effects of the continuous-time dynamics of cellular neural networks.
A cellular neural network differs from Hopfield's neural networks and a cellular automata machine in the following ways: